Benchmarking long-read RNA-sequencing analysis tools using in silico mixtures
- Author(s)
- Dong, X; Du, MRM; Gouil, Q; Tian, L; Jabbari, JS; Bowden, R; Baldoni, PL; Chen, Y; Smyth, GK; Amarasinghe, SL; Law, CW; Ritchie, ME;
- Details
- Publication Year 2023-10-02,Volume 20,Issue #11,Page 1810-1821
- Journal Title
- Nature Methods
- Abstract
- The lack of benchmark data sets with inbuilt ground-truth makes it challenging to compare the performance of existing long-read isoform detection and differential expression analysis workflows. Here, we present a benchmark experiment using two human lung adenocarcinoma cell lines that were each profiled in triplicate together with synthetic, spliced, spike-in RNAs (sequins). Samples were deeply sequenced on both Illumina short-read and Oxford Nanopore Technologies long-read platforms. Alongside the ground-truth available via the sequins, we created in silico mixture samples to allow performance assessment in the absence of true positives or true negatives. Our results show that StringTie2 and bambu outperformed other tools from the six isoform detection tools tested, DESeq2, edgeR and limma-voom were best among the five differential transcript expression tools tested and there was no clear front-runner for performing differential transcript usage analysis between the five tools compared, which suggests further methods development is needed for this application.
- Publisher
- NPG
- Keywords
- Humans; *Gene Expression Profiling/methods; *High-Throughput Nucleotide Sequencing/methods; Benchmarking/methods; Rna; Protein Isoforms
- Research Division(s)
- Advanced Technology And Biology; Bioinformatics; Epigenetics And Development; Cancer Biology And Stem Cells; Epigenetics and Development; Advanced Technology and Biology; Bioinformatics
- PubMed ID
- 37783886
- Publisher's Version
- https://doi.org/10.1038/s41592-023-02026-3
- Terms of Use/Rights Notice
- Refer to copyright notice on published article.
Creation Date: 2023-11-15 10:47:38
Last Modified: 2023-11-20 03:24:05